My City Report for road managers

1.3 Statistics, Machine Learning, Data Assimilation, Algorithms, Mathematical Foundation, Data Mining
1.4 Visualization, Visual Analytics
2.2 Manufacturing Science and Engineering (Data-Driven Simulation, Structural Analysis, Fluid Analysis, Skills Transfer, Connected Industry)
2.4 Smart City (Transportation, Town Planning, Living Environment, Crime Prevention)
2.5 Geospatial Information (Remote Sensing, People Flow)
2.6 Market of Data (Data Trading, Data-driven Value Co-Creation)
2.7 Disaster Prevention, Reconstruction (Earthquake, Tsunami, Volcano, Meteorology, Flood, Natural Disaster)

Yoshihide Sekimoto

Center for Spatial Information Science

Professor Project Professor, Institute of Industrial Science

By collecting road images using smartphones (or driving recorders) mounted on vehicles and detecting road damages via emerging artificial intelligence (AI) techniques, the My City Report for road managers project aims to realize comprehensive real-time grasping of road-damage situations and optimize the inspection efficiency. As a part of the My City Report consortium, this project launched and provided services to citizens in 13 municipalities with MCR (a civic collaboration contribution service).

Examples of a road-damage type that can be obtained using a smartphone
Sekimoto Laboratory, The University of Tokyo

Data and dashboard for MyCityReport
Sekimoto Laboratory, The University of Tokyo

Research collaborators

Urban X Technologies, Inc.
Association for Promotion of Infrastructure Geospatial Information Distribution

Related publications

Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., and Omata, H., Generative adversarial network for road damage detection, Computer-Aided Civil and Infrastructure Engineering, Wiley, 36, 47-60, 2021 (Available online: 2 June, 2020).

Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., and Omata, H., Road damage detection and classification using deep neural networks with smartphone images, Computer‐Aided Civil and Infrastructure Engineering, 33(12), 1127-1141, 2018.

Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., and Omata, H., A framework for Clack detection and maintenance criteria extraction using machine learning and smartphone camera, JSTE Journal of Traffic Engineering, 4(3), A_1-A_8 2018 (in Japanese).